Each layer on a CNN produces a feature map with decreasing spatial resolution but with stronger semantic features as the convolution progress to the end of the network. Lin et al. [1]} realised that some key information was abandoned on each ConvNet stage, simply because of the reduction in the feature map resolution which ultimately leads to a higher uncertainty in the position of the object. In this way, they thought that such intermediate features could be used to improve the accuracy of CNNs without the penalties of speed and memory resources that were typical in other applications, as they are already part of the CNN pipeline and, hence, it is also independent of the backbone convolutional architecture.
It is common that CNNs have several layers that output same feature size, such group of layers is known as a stage or block. The last layer feature map on a stage, denoted as \(C_2,C_3,C_4,C_5\)  for conv2, conv3, conv4 or conv5 outputs, have strides of 4, 8, 16, 32 pixels with respect to the input image. This last feature map, which corresponds on a Bottom-Up Pathway, is then used to create the so call Feature Pyramid Network (FPN). Each feature of the pyramid has:
